



'''simple docstring'''



from jiwer import compute_measures

import datasets


lowerCAmelCase_  :							List[str]             =    '\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

lowerCAmelCase_  :							Any             =    '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

lowerCAmelCase_  :							Any             =    '\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,					_KWARGS_DESCRIPTION      )
class 						lowerCamelCase_				(       datasets.Metric      ):







						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													return datasets.MetricInfo(
													    description=_DESCRIPTION					,     citation=_CITATION					,     inputs_description=_KWARGS_DESCRIPTION					,     features=datasets.Features(
													        {
													            '''predictions''': datasets.Value('''string'''					,     id='''sequence'''					),
													            '''references''': datasets.Value('''string'''					,     id='''sequence'''					),
													        }					)					,     codebase_urls=['''https://github.com/jitsi/jiwer/''']					,     reference_urls=[
													        '''https://en.wikipedia.org/wiki/Word_error_rate''',
													    ]					,     )







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   Optional[int]=None					,     lowerCAmelCase__ :   Tuple=None					,     lowerCAmelCase__ :   str=False					):

													"""simple docstring"""
													if concatenate_texts:
																				return compute_measures(lowerCAmelCase__					,     lowerCAmelCase__					)["wer"]
													else:
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      0
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      0
																				for prediction, reference in zip(lowerCAmelCase__					,     lowerCAmelCase__					):
																											SCREAMING_SNAKE_CASE       :   List[str]         =      compute_measures(lowerCAmelCase__					,     lowerCAmelCase__					)
																											incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																											total += measures["substitutions"] + measures["deletions"] + measures["hits"]
																				return incorrect / total









'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase_  :							Optional[int]             =    logging.get_logger(__name__)

lowerCAmelCase_  :							List[str]             =    {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowerCAmelCase_  :							Any             =    [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]
def        UpperCAmelCase (    A	:					Dict					):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {}
							with open(A       ,		'''r'''					) as file:
														for line_number, line in enumerate(A					):
																					SCREAMING_SNAKE_CASE       :   Dict         =      line.strip()
																					if line:
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      line.split()
																												SCREAMING_SNAKE_CASE       :   Any         =      line_number
																												SCREAMING_SNAKE_CASE       :   Any         =      words[0]
																												SCREAMING_SNAKE_CASE       :   Optional[Any]         =      value
							return result
def        UpperCAmelCase (    A	:					Union[str, Any]       ,		A	:					Tuple       ,		A	:					List[Any]       ,		A	:					str       ,		A	:					int					):
							for attribute in key.split('''.'''					):
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      getattr(A       ,		A					)

							SCREAMING_SNAKE_CASE       :   Any         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      PARAM_MAPPING[full_name.split('''.'''					)[-1]]
																					SCREAMING_SNAKE_CASE       :   Tuple         =      '''param'''

							if weight_type is not None and weight_type != "param":
														SCREAMING_SNAKE_CASE       :   List[Any]         =      getattr(A       ,		A					).shape
							elif weight_type is not None and weight_type == "param":
														SCREAMING_SNAKE_CASE       :   List[str]         =      hf_pointer
														for attribute in hf_param_name.split('''.'''					):
																					SCREAMING_SNAKE_CASE       :   int         =      getattr(A       ,		A					)
														SCREAMING_SNAKE_CASE       :   int         =      shape_pointer.shape

														# let's reduce dimension
														SCREAMING_SNAKE_CASE       :   Dict         =      value[0]
							else:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
														    F""" {value.shape} for {full_name}"""					)

							if weight_type == "weight":
														SCREAMING_SNAKE_CASE       :   str         =      value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE       :   List[str]         =      value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE       :   Dict         =      value
							elif weight_type == "param":
														for attribute in hf_param_name.split('''.'''					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      getattr(A       ,		A					)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      value
							else:
														SCREAMING_SNAKE_CASE       :   Tuple         =      value

							logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""					)
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					List[str]       ,		A	:					Optional[int]       ,		A	:					Tuple       ,		A	:					Tuple					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      PARAM_MAPPING[full_name.split('''.'''					)[-1]]
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      '''param'''

							if weight_type is not None and weight_type != "param":
														SCREAMING_SNAKE_CASE       :   str         =      '''.'''.join([key, weight_type]					)
							elif weight_type is not None and weight_type == "param":
														SCREAMING_SNAKE_CASE       :   Tuple         =      '''.'''.join([key, hf_param_name]					)
							else:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      key

							SCREAMING_SNAKE_CASE       :   int         =      value if '''lm_head''' in full_key else value[0]


lowerCAmelCase_  :							Union[str, Any]             =    {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}
def        UpperCAmelCase (    A	:					List[Any]       ,		A	:					Tuple       ,		A	:					Dict=None       ,		A	:					Optional[int]=None					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      False
							for key, mapped_key in MAPPING.items():
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
														if key in name or key.split('''w2v_model.'''					)[-1] == name.split('''.'''					)[0]:
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      True
																					if "*" in mapped_key:
																												SCREAMING_SNAKE_CASE       :   int         =      name.split(A					)[0].split('''.'''					)[-2]
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      mapped_key.replace('''*'''       ,		A					)
																					if "weight_g" in name:
																												SCREAMING_SNAKE_CASE       :   List[Any]         =      '''weight_g'''
																					elif "weight_v" in name:
																												SCREAMING_SNAKE_CASE       :   int         =      '''weight_v'''
																					elif "bias" in name:
																												SCREAMING_SNAKE_CASE       :   str         =      '''bias'''
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''weight'''
																					else:
																												SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
																					if hf_dict is not None:
																												rename_dict(A       ,		A       ,		A       ,		A       ,		A					)
																					else:
																												set_recursively(A       ,		A       ,		A       ,		A       ,		A					)
																					return is_used
							return is_used
def        UpperCAmelCase (    A	:					Optional[int]       ,		A	:					Any       ,		A	:					int					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      []
							SCREAMING_SNAKE_CASE       :   Any         =      fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE       :   List[str]         =      hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE       :   str         =      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A       ,		A       ,		A       ,		A       ,		hf_model.config.feat_extract_norm == '''group'''       ,		)
																					SCREAMING_SNAKE_CASE       :   List[str]         =      True
														else:
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_wavaveca_layer(A       ,		A       ,		A					)
														if not is_used:
																					unused_weights.append(A					)

							logger.warning(F"""Unused weights: {unused_weights}"""					)
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					int       ,		A	:					Tuple       ,		A	:					int       ,		A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   str         =      full_name.split('''conv_layers.'''					)[-1]
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      name.split('''.'''					)
							SCREAMING_SNAKE_CASE       :   Tuple         =      int(items[0]					)
							SCREAMING_SNAKE_CASE       :   int         =      int(items[1]					)

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   str         =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
							else:
														unused_weights.append(A					)
@torch.no_grad()
def        UpperCAmelCase (    A	:					Any       ,		A	:					Union[str, Any]       ,		A	:					Optional[Any]=None       ,		A	:					Any=None       ,		A	:					Any=True       ,		A	:					str=False					):
							if config_path is not None:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      WavaVecaConfig.from_pretrained(A					)
							else:
														SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaConfig()

							if is_seq_class:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      read_txt_into_dict(A					)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      idalabel
														SCREAMING_SNAKE_CASE       :   Tuple         =      WavaVecaForSequenceClassification(A					)
														SCREAMING_SNAKE_CASE       :   Tuple         =      WavaVecaFeatureExtractor(
														    feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=A       ,		return_attention_mask=A       ,		)
														feature_extractor.save_pretrained(A					)

							elif is_finetuned:
														if dict_path:
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      Dictionary.load(A					)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					SCREAMING_SNAKE_CASE       :   List[str]         =      target_dict.pad_index
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      target_dict.bos_index
																					SCREAMING_SNAKE_CASE       :   Dict         =      target_dict.eos_index
																					SCREAMING_SNAKE_CASE       :   Dict         =      len(target_dict.symbols					)
																					SCREAMING_SNAKE_CASE       :   str         =      os.path.join(A       ,		'''vocab.json'''					)
																					if not os.path.isdir(A					):
																												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(A					)					)
																												return
																					os.makedirs(A       ,		exist_ok=A					)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					SCREAMING_SNAKE_CASE       :   str         =      0
																					SCREAMING_SNAKE_CASE       :   Tuple         =      1
																					with open(A       ,		'''w'''       ,		encoding='''utf-8'''					) as vocab_handle:
																												json.dump(A       ,		A					)
																					SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaCTCTokenizer(
																					    A       ,		unk_token=target_dict.unk_word       ,		pad_token=target_dict.pad_word       ,		bos_token=target_dict.bos_word       ,		eos_token=target_dict.eos_word       ,		word_delimiter_token='''|'''       ,		do_lower_case=A       ,		)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      True if config.feat_extract_norm == '''layer''' else False
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      WavaVecaFeatureExtractor(
																					    feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=A       ,		return_attention_mask=A       ,		)
																					SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaProcessor(feature_extractor=A       ,		tokenizer=A					)
																					processor.save_pretrained(A					)

														SCREAMING_SNAKE_CASE       :   List[str]         =      WavaVecaForCTC(A					)
							else:
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      WavaVecaForPreTraining(A					)

							if is_finetuned or is_seq_class:
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[Any]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]       ,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''					)[:-1]					)}					)
							else:
														SCREAMING_SNAKE_CASE       :   str         =      argparse.Namespace(task='''audio_pretraining'''					)
														SCREAMING_SNAKE_CASE       :   Dict         =      fairseq.tasks.setup_task(A					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[str]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,		task=A					)

							SCREAMING_SNAKE_CASE       :   List[Any]         =      model[0].eval()

							recursively_load_weights(A       ,		A       ,		not is_finetuned					)

							hf_wavavec.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							List[Any]             =    argparse.ArgumentParser()
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
							parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
							parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
							parser.add_argument(
							    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
							)
							parser.add_argument(
							    '--is_seq_class',
							    action='store_true',
							    help='Whether the model to convert is a fine-tuned sequence classification model or not',
							)
							lowerCAmelCase_  :							Union[str, Any]             =    parser.parse_args()

							lowerCAmelCase_  :							Dict             =    not args.not_finetuned and not args.is_seq_class
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.dict_path,
							    is_finetuned,
							    args.is_seq_class,
							)




